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Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. (arXiv:2103.04715v5 [stat.ME] UPDATED)
Jan. 12, 2022, 2:10 a.m. | Rémi Laumont, Valentin de Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
stat.ML updates on arXiv.org arxiv.org
Since the seminal work of Venkatakrishnan et al. (2013), Plug & Play (PnP)
methods have become ubiquitous in Bayesian imaging. These methods derive
Minimum Mean Square Error (MMSE) or Maximum A Posteriori (MAP) estimators for
inverse problems in imaging by combining an explicit likelihood function with a
prior that is implicitly defined by an image denoising algorithm. The PnP
algorithms proposed in the literature mainly differ in the iterative schemes
they use for optimisation or for sampling. In the case …
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